https://github.com/harshita2234/potato-prices-prediction
Project aims to forecast potato prices in India using LSTM, KNN, and Random Forest Regression, integrating historical data on prices, regional stats, and rainfall patterns. Targeting agricultural stakeholders for informed decision-making.
https://github.com/harshita2234/potato-prices-prediction
csv-files data-cleaning-and-preprocessing data-mining-python k-nearest-neighbours knn long-short-term-memory lstm machine-learning-algorithms predictive-modeling python3 random-forest-regression random-forest-regressor
Last synced: 7 months ago
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Project aims to forecast potato prices in India using LSTM, KNN, and Random Forest Regression, integrating historical data on prices, regional stats, and rainfall patterns. Targeting agricultural stakeholders for informed decision-making.
- Host: GitHub
- URL: https://github.com/harshita2234/potato-prices-prediction
- Owner: harshita2234
- License: mit
- Created: 2024-05-03T08:44:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-01T06:46:46.000Z (over 1 year ago)
- Last Synced: 2025-01-30T00:31:19.470Z (9 months ago)
- Topics: csv-files, data-cleaning-and-preprocessing, data-mining-python, k-nearest-neighbours, knn, long-short-term-memory, lstm, machine-learning-algorithms, predictive-modeling, python3, random-forest-regression, random-forest-regressor
- Language: Python
- Homepage:
- Size: 862 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Potato Prices Prediction
This repository contains the code and data for predicting potato prices based on various factors such as rainfall.
## Project Overview
This project aims to predict the prices of potatoes using historical data. The model can help farmers and traders make informed decisions by analysing patterns and trends.
## Project Steps
1. **Data Collection and Preprocessing**: We used three datasets - `potato.csv`, `rainfall_news.csv`, and `state.csv` to create the final dataset `final_potato_rainfall_data.csv`. `preprocessing-steps.py` explain how to do so.
2. **Data Cleaning**: The final dataset was cleaned to ensure accuracy and reliability. The steps are as follows:
```python
import pandas as pd
# Load the final output CSV file
final_data = pd.read_csv('final_potato_rainfall_data.csv')
# Remove rows where any key field is NaN
final_data_cleaned = final_data.dropna(subset=['state', 'date', 'rainfall', 'price'])
# Save the cleaned final output back to a CSV file
final_data_cleaned.to_csv('final_potato_rainfall_data_cleaned.csv', index=False)
print("Data cleaning complete. Clean output saved to 'final_potato_rainfall_data_cleaned.csv'.")
```
3. **Modeling**: The cleaned data was used to train the following models:
- K-Nearest Neighbors (KNN)
- Long Short-Term Memory (LSTM)
- Random Forest Regressor
## Installation
You need to have Python installed to run the code in this repository. You can install the necessary libraries using the following command:
```bash
pip install pandas numpy scikit-learn matplotlib seaborn tensorflow
```
## Usage
1. Clone this repository:
```bash
git clone https://github.com/harshita2234/Potato-Prices-Prediction.git
```
2. Navigate to the project directory:
```bash
cd Potato-Prices-Prediction
```
3. Ensure you have the cleaned data file in the appropriate directory:
```bash
mv final_potato_rainfall_data_cleaned.csv .
```
4. Run the models:
```bash
python knn.py
python lstm.py
python random_forest_regressor.py
```
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for improvements or bug fixes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Acknowledgements
Thanks to all the contributors and data providers for their invaluable support in making this project possible.